La minería de datos en apoyo a la toma de decisiones clínicas

Yidier  Romero Zaldivar; José Felipe Ramírez Pérez; Lissette Soto Pelegrín

Autores/as

Yidier Romero Zaldivar Universidad de las Ciencias Informáticas
José Felipe Ramírez Pérez Universidad Autónoma de Baja California
Lissette Soto Pelegrín Universidad de las Ciencias Informáticas

Palabras clave:

minería de datos, ; modelos de predicción, sistemas de información clínica

Resumen

Las técnicas de minería de datos constituyen una herramienta a tener presente cuando se realice un
análisis predictivo. En el área de la medicina clínica se aplican estas técnicas de minería de datos
predictivas para apoyar la toma de decisiones de los médicos en el diagnóstico de enfermedades,
para el pronóstico de supervivencia de los pacientes y para sugerir tratamientos. Los autores de
este trabajo se plantearon como objetivo realizar una revisión de la literatura para identificar las
tendencias en el tema, las técnicas más precisas en la tarea de predicción y su aplicación en la
medicina clínica. Para dar cumplimiento al objetivo propuesto se aplicó un método de revisión
sistemática de la literatura (SLR, del inglés). Al culminar el trabajo se identificaron tres criterios
importantes para elegir un modelo efectivo en el análisis predictivo utilizando datos clínicos: la
representación del problema, el poder explicativo de su salida y la capacidad de adicionar
conocimiento previo de los expertos del dominio.

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